FEMA National Risk Index
Data for Charlottesville Region
Michele Claibourn
11/13/2021
Data Source
Source: FEMA, National Risk Index, October 2020 release.
- Download URL: https://nri-data-downloads.s3.amazonaws.com/NRI_Table_CensusTracts.zip
- To learn more: National Risk Index Primer, FEMA, December 2020
- To learn more: National Risk Index Platform and Data
- Data is from the July 2021 release. There is not set update schedule.
About the data
The National Risk Index is intended to provide a view of the natural hazard risk within communities. While FEMA includes information on 18 natural hazards, we focus on six – coastal flooding, drought, heat wave, hurricane, riverine flooding, and strong wind – pulling measures on
- frequency (measuring the number of events or event days during a reporting period and the estimated annualized frequency or probability),
- exposure (measuring the building value, people, or agricultural value exposed to the natural hazard event), and
- historic loss ratio (measuring the proportion of building value, people, or agricultural value that has been historically impacted by the natural hazard).
The NRI uses data on natural hazards from multiple sources and estimates natural hazard frequency, exposure, and historic loss at the census tract level.
Variable descriptions
glimpse(nri)## Rows: 50
## Columns: 76
## $ OID_ <dbl> 47346, 47471, 48413, 48503, 48508, 48509, 48904, 48905, 489…
## $ NRI_ID <chr> "T51065020101", "T51065020300", "T51003010201", "T510790301…
## $ STATE <chr> "Virginia", "Virginia", "Virginia", "Virginia", "Virginia",…
## $ STATEABBRV <chr> "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA",…
## $ STATEFIPS <dbl> 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51,…
## $ COUNTY <chr> "Fluvanna", "Fluvanna", "Albemarle", "Greene", "Albemarle",…
## $ COUNTYTYPE <chr> "County", "County", "County", "County", "County", "County",…
## $ COUNTYFIPS <chr> "065", "065", "003", "079", "003", "003", "003", "003", "07…
## $ STCOFIPS <dbl> 51065, 51065, 51003, 51079, 51003, 51003, 51003, 51003, 510…
## $ TRACT <chr> "020101", "020300", "010201", "030102", "010800", "011000",…
## $ TRACTFIPS <dbl> 51065020101, 51065020300, 51003010201, 51079030102, 5100301…
## $ POPULATION <dbl> 5571, 5311, 4664, 5393, 5325, 6292, 3765, 3738, 9145, 9341,…
## $ BUILDVALUE <dbl> 551401000, 530703000, 589443000, 569304000, 707799000, 1265…
## $ AGRIVALUE <dbl> 1000124.0179, 2454071.5733, 998608.4291, 2592134.2808, 1253…
## $ AREA <dbl> 43.2172270, 101.6045726, 27.0136979, 63.8718600, 5.3042504,…
## $ CFLD_EVNTS <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_AFREQ <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_EXPB <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_EXPP <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_EXPPE <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_EXPT <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_HLRB <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_HLRP <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CFLD_HLRR <chr> "Not Applicable", "Not Applicable", "Not Applicable", "Not …
## $ DRGT_EVNTS <dbl> 126, 154, 70, 70, 77, 77, 91, 91, 63, 105, 154, 70, 98, 126…
## $ DRGT_AFREQ <dbl> 7.000000, 8.555556, 3.888889, 3.888889, 4.277778, 4.277778,…
## $ DRGT_EXPB <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ DRGT_EXPP <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ DRGT_EXPPE <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ DRGT_EXPA <dbl> 611186.8998, 2096304.6178, 898747.5862, 2592134.2808, 12536…
## $ DRGT_EXPT <dbl> 611186.8998, 2096304.6178, 898747.5862, 2592134.2808, 12536…
## $ DRGT_HLRB <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ DRGT_HLRP <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ DRGT_HLRA <dbl> 0.003197017, 0.003197017, 0.003222722, 0.004763245, 0.00322…
## $ DRGT_HLRR <chr> "Very High", "Very High", "Very High", "Very High", "Very H…
## $ HWAV_EVNTS <dbl> 8, 8, 6, 6, 6, 6, 6, 6, 12, 8, 5, 6, 6, 8, 5, 12, 6, 6, 6, …
## $ HWAV_AFREQ <dbl> 0.6589786, 0.6589786, 0.4942339, 0.3947789, 0.4942339, 0.49…
## $ HWAV_EXPB <dbl> 551400513, 530702957, 589442851, 546678926, 707799000, 1265…
## $ HWAV_EXPP <dbl> 5570.997, 5311.000, 4663.998, 5231.000, 5325.000, 6291.996,…
## $ HWAV_EXPPE <dbl> 41225376461, 39301398589, 34513586247, 38709396581, 3940500…
## $ HWAV_EXPT <dbl> 41776776974, 39832101546, 35103029097, 39256075507, 4011279…
## $ HWAV_HLRB <dbl> 1.169e-12, 1.169e-12, 1.169e-12, 1.169e-12, 1.169e-12, 1.16…
## $ HWAV_HLRP <dbl> 3.971792e-07, 3.971792e-07, 1.058701e-07, 5.350162e-07, 1.0…
## $ HWAV_HLRR <chr> "Very Low", "Very Low", "Very Low", "Relatively Low", "Very…
## $ HRCN_EVNTS <dbl> 6, 7, 9, 9, 9, 9, 9, 9, 9, 6, 8, 9, 9, 5, 8, 9, 8, 8, 8, 4,…
## $ HRCN_AFREQ <dbl> 0.06582491, 0.06589680, 0.07046340, 0.07180899, 0.06582491,…
## $ HRCN_EXPB <dbl> 550733910, 530380270, 589442851, 569303923, 704074505, 1265…
## $ HRCN_EXPP <dbl> 5564.073, 5307.788, 4663.998, 5393.000, 5312.928, 6291.648,…
## $ HRCN_EXPPE <dbl> 41174136929, 39277633809, 34513586247, 39908196307, 3931566…
## $ HRCN_EXPT <dbl> 41724870839, 39808014079, 35103029097, 40477500230, 4001973…
## $ HRCN_HLRB <dbl> 0.0001627591, 0.0001627591, 0.0001627591, 0.0001627591, 0.0…
## $ HRCN_HLRP <dbl> 1.432476e-06, 1.432476e-06, 1.432476e-06, 1.599039e-06, 1.4…
## $ HRCN_HLRR <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ RFLD_EVNTS <dbl> 3, 3, 59, 37, 59, 59, 59, 59, 37, 3, 44, 59, 59, 7, 44, 59,…
## $ RFLD_AFREQ <dbl> 0.1363636, 0.1363636, 2.6818182, 1.6818182, 2.6818182, 2.68…
## $ RFLD_EXPB <dbl> 1649205.9, 3600534.0, 14450941.1, 3620317.5, 699096.0, 2420…
## $ RFLD_EXPP <dbl> 11.836098, 35.576304, 102.666978, 36.554255, 5.629246, 120.…
## $ RFLD_EXPPE <dbl> 87587125, 263264651, 759735634, 270501484, 41656421, 893340…
## $ RFLD_EXPA <dbl> 50281.8794, 168074.2006, 44928.6765, 85771.0354, 488.6131, …
## $ RFLD_EXPT <dbl> 89286613, 267033259, 774231504, 274207572, 42356006, 917627…
## $ RFLD_HLRB <dbl> 2.251579e-04, 2.251579e-04, 4.212615e-05, 2.905738e-03, 4.2…
## $ RFLD_HLRP <dbl> 4.003475e-05, 4.003475e-05, 9.587986e-06, 4.040050e-06, 9.5…
## $ RFLD_HLRA <dbl> 0.009312590, 0.009312590, 0.011180959, 0.009741927, 0.01118…
## $ RFLD_HLRR <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ SWND_EVNTS <dbl> 369, 368, 390, 396, 368, 369, 369, 368, 397, 369, 363, 369,…
## $ SWND_AFREQ <dbl> 11.53125, 11.52825, 12.20950, 12.40625, 11.53125, 11.53125,…
## $ SWND_EXPB <dbl> 551401000, 530703000, 589443000, 569304000, 707799000, 1265…
## $ SWND_EXPP <dbl> 5571, 5311, 4664, 5393, 5325, 6292, 3765, 3738, 9145, 9341,…
## $ SWND_EXPPE <dbl> 41225400000, 39301400000, 34513600000, 39908200000, 3940500…
## $ SWND_EXPA <dbl> 1000124.0179, 2454071.5733, 998608.4291, 2592134.2808, 1253…
## $ SWND_EXPT <dbl> 41777801124, 39834557072, 35104041608, 40480096134, 4011292…
## $ SWND_HLRB <dbl> 8.246272e-06, 8.246272e-06, 2.383243e-06, 7.876500e-06, 2.3…
## $ SWND_HLRP <dbl> 2.889675e-07, 2.889675e-07, 3.395608e-07, 2.939429e-07, 3.3…
## $ SWND_HLRA <dbl> 0.0001960432, 0.0001960432, 0.0003132078, 0.0002843516, 0.0…
## $ SWND_HLRR <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ NRI_VER <chr> "October 2020", "October 2020", "October 2020", "October 20…Observations are census tract estimates of…
- Population, building value, agricultural value, and area within tract
- Natural hazards include: CFLD - coastal flooding, DRGT - drought, HWAV - heat wave, HRCN - hurricane, RFLD - riverine flooding, SWND - strong wind
- Hazard measures include: EVNTS - number of events in recording period, AFREQ - annualized frequency (# events/# years in recording period)
- Exposure measures include: EXPB - building value exposure, EXPP - population exposure, EXPE - population equivalence exposure, EXPA - agricultural value exposure
- Historic loss ratio measures include: HLRB - historic loss ratio for building value, HLRA - historicla loss ratio for agriculture, HLRP - historical loss ratio for population, HLRR - historic loss ratio overall
Summaries
5-number summaries of (non-missing) numeric variables (remove tract identifiers)
nri %>% select(-c(OID_:STATEFIPS, COUNTYTYPE:TRACTFIPS, NRI_VER)) %>%
select(where(~is.numeric(.x) && !is.na(.x))) %>%
as.data.frame() %>%
stargazer(., type = "text", title = "Summary Statistics", digits = 0,
summary.stat = c("mean", "sd", "min", "median", "max"))##
## Summary Statistics
## =====================================================================================
## Statistic Mean St. Dev. Min Median Max
## -------------------------------------------------------------------------------------
## POPULATION 4,694 1,741 1,900 4,382 9,341
## BUILDVALUE 600,914,960 274,394,213 210,947,000 559,413,000 1,352,245,000
## AGRIVALUE 1,701,520 2,542,400 0 931,421 13,226,654
## AREA 43 53 0 18 195
## DRGT_EVNTS 97 24 63 91 161
## DRGT_AFREQ 5 1 4 5.1 9
## DRGT_EXPA 1,372,734 2,012,315 0 596,796 8,841,211
## DRGT_EXPT 1,372,734 2,012,315 0 596,796 8,841,211
## DRGT_HLRA 0 0 0 0 0
## HWAV_EVNTS 7 2 5 6 12
## HWAV_AFREQ 1 0 0 0 1
## HWAV_EXPB 594,263,002 268,213,179 210,947,000 549,039,719 1,352,244,990
## HWAV_EXPP 4,678 1,740 1,900 4,351 9,341
## HWAV_EXPPE 34,615,220,205 12,878,768,794 14,060,000,000 32,197,399,778 69,123,399,489
## HWAV_EXPT 35,209,483,206 13,089,228,055 14,270,947,000 32,668,421,302 70,475,644,479
## HWAV_HLRB 0 0 0 0 0
## HWAV_HLRP 0 0 0 0 0
## HRCN_EVNTS 8 1 4 9 9
## HRCN_AFREQ 0 0 0 0 0
## HRCN_EXPB 599,680,668 274,451,546 210,947,000 559,079,443 1,349,737,674
## HRCN_EXPP 4,689 1,744 1,900 4,382 9,321
## HRCN_EXPPE 34,695,606,309 12,903,118,962 14,060,000,000 32,426,797,882 68,974,004,894
## HRCN_EXPT 35,295,286,977 13,115,107,535 14,270,947,000 32,941,525,397 70,323,742,568
## HRCN_HLRB 0 0 0 0 0
## HRCN_HLRP 0 0 0 0 0
## RFLD_EVNTS 30 25 0 15 59
## RFLD_AFREQ 1 1 0 1 3
## RFLD_EXPB 13,388,752 13,835,303 0 8,427,838 54,305,747
## RFLD_EXPP 89 93 0 62 416
## RFLD_EXPPE 661,263,174 690,685,745 0 460,988,960 3,081,542,905
## RFLD_EXPA 153,755 313,700 0 38,504 1,708,403
## RFLD_EXPT 674,805,682 703,954,174 0 469,495,154 3,137,557,055
## RFLD_HLRB 0 0 0 0 0
## RFLD_HLRP 0 0 0 0 0
## RFLD_HLRA 0 0 0 0 0
## SWND_EVNTS 369 9 354 368 397
## SWND_AFREQ 12 0 11 12 12
## SWND_EXPB 600,914,960 274,394,213 210,947,000 559,413,000 1,352,245,000
## SWND_EXPP 4,694 1,741 1,900 4,382 9,341
## SWND_EXPPE 34,737,376,000 12,882,414,388 14,060,000,000 32,426,800,000 69,123,400,000
## SWND_EXPA 1,701,520 2,542,400 0 931,421 13,226,654
## SWND_EXPT 35,339,992,480 13,094,503,181 14,270,950,433 32,954,976,874 70,475,710,779
## SWND_HLRB 0 0 0 0 0
## SWND_HLRP 0 0 0 0 0
## SWND_HLRA 0 0 0 0 0
## -------------------------------------------------------------------------------------Visual distribution
Frequency distribution across tracts:
Tract assets
nri %>% select(TRACTFIPS:AREA) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
geom_histogram() +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% c("POPULATION", "BUILDVALUE", "AGRIVALUE")) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “POPULATION: Population exposure is defined as the estimated number of people to be exposed to a hazard. The maximum possible population exposure of an area is its population as recorded in Hazus 4.2 SP1 (https://msc.fema.gov/portal/resources/hazus).”
[2] “BUILDVALUE: Building exposure is defined as the dollar value of the buildings exposed to a hazard. The maximum possible building exposure of an area is its building value as recorded in Hazus 4.2 (https://msc.fema.gov/portal/resources/hazus).”
[3] “AGRIVALUE: Agriculture exposure is defined as the estimated dollar value of the crops and livestock exposed to a hazard. This is derived from the USDA 2017 Census of Agriculture county-level value of crop and pastureland (https://www.nass.usda.gov/Publications/AgCensus/2017/index.php).”
Tract hazards: Drought
vars <- nri %>% select(contains("DRGT"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “DRGT_EVNTS: A Drought is a deficiency of precipitation over an extended period of time resulting in a water shortage. The number of Drought events are the number of days from 2000-2017 in which an area experinced extreme drought or exceptional drought as identified by the National Drought Mitigation Center, U.S. Drought Monitor (https://droughtmonitor.unl.edu/).”
[2] “DRGT_AFREQ: A Drought is a deficiency of precipitation over an extended period of time resulting in a water shortage. The annualized freqeuncy of Drought events are the average number of days per year (based on historical droughts from 2000-2017 ) in which an area experinced extreme drought or exceptional drought as identified by the National Drought Mitigation Center, U.S. Drought Monitor (https://droughtmonitor.unl.edu/).” [3] “DRGT_EXPA: Based on the intersection of the Drought polygon and each area, multipled by the area’s total agricultural value density.”
[4] “DRGT_HLRA: The Historic Loss Ratio is the representative percentage of a location’s agricultural exposure area that experiences loss due to a Drought event-day, or the average rate of loss associated with the occurrence of a Drought event-day.”
Tract hazards: Heat Wave
vars <- nri %>% select(contains("HWAV"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “HWAV_EVNTS: A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. The number of Heat Wave event-days are the number of days from 2005-2017 in which an area received an Excessive Heat or Heat alert by the National Weather Services, as compiled by the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/request/gis/watchwarn.phtml).”
[2] “HWAV_AFREQ: A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. The annualized frequency of Heat Wave events are the average number of days per year (based on historical events from 2005-2017) for which an area received an Excessive Heat or Heat alert by the National Weather Services, as compiled by the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/request/gis/watchwarn.phtml).” [3] “HWAV_EXPB: Based on the intersection of the Heat Wave region and each area, multipled by the area’s building value density.”
[4] “HWAV_EXPP: Based on the intersection of the Heat Wave region and each area, multipled by the area’s population density.”
[5] “HWAV_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “HWAV_EXPT: An aggregation of population exposure and building exposure to a hazard.”
[7] “HWAV_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Heat Wave event-day, or the average rate of loss associated with the occurrence of a Heat Wave event-day.”
[8] “HWAV_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Heat Wave event-day, or the average rate of loss associated with the occurrence of a Heat Wave event-day.”
Tract hazards: Hurricane
vars <- nri %>% select(contains("HRCN"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “HRCN_EVNTS: A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front and maximum sustained winds of at least 74 miles per hour; also included are tropical storms for which wind speeds range from 39 to 74 mph. The number of events are the number of events occuring between 1851 and 2017 as complied by the National Hurrican Center’s HURDAT2 dataset (https://www.nhc.noaa.gov/data/).”
[2] “HRCN_AFREQ: A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front and maximum sustained winds of at least 74 miles per hour; also included are tropical storms for which wind speeds range from 39 to 74 mph. The annualized frequency of Hurricane events are the average number of events per year (based on historical events from 1851 and 2017) experienced by an area as complied by the National Hurrican Center’s HURDAT2 dataset (https://www.nhc.noaa.gov/data/).” [3] “HRCN_EXPB: Based on the intersection of the buffered Huricane pahts and each area, multipled by the area’s building value density.”
[4] “HRCN_EXPP: Based on the intersection of the buffered Huricane pahts and each area, multipled by the area’s population density.”
[5] “HRCN_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “HRCN_EXPT: An aggregation of population exposure and building exposure to a hazard.”
[7] “HRCN_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Hurrican event, or the average rate of loss associated with the occurrence of a Hurricane event.”
[8] “HRCN_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Hurrican event, or the average rate of loss associated with the occurrence of a Hurricane event.”
Tract hazards: Riverine Flooding
vars <- nri %>% select(contains("RFLD"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “RFLD_EVNTS: Riverine Flooding is when streams and rivers exceed the capacity of their natural or constructed channels to accommodate water flow and water overflows the banks, spilling into adjacent low-lying, dry land. The number of events are the number of events occuring between 1995 and 2016 in an area as recorded in the National Weather Service Storm Events Database (https://www.ncdc.noaa.gov/stormevents/).”
[2] “RFLD_AFREQ: Riverine Flooding is when streams and rivers exceed the capacity of their natural or constructed channels to accommodate water flow and water overflows the banks, spilling into adjacent low-lying, dry land. The annualized freqeuncy of Riverine Flooding events are the average number of events per year (based on historical events from 1995 and 2016) experienced by an area as recorded in the National Weather Service Storm Events Database (https://www.ncdc.noaa.gov/stormevents/).” [3] “RFLD_EXPB: Based on the intersection of the Riverine flooding region and each area, multipled by the area’s building value density.”
[4] “RFLD_EXPP: Based on the intersection of the Riverine flooding region and each area, multipled by the area’s population density.”
[5] “RFLD_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “RFLD_EXPA: Based on the intersection of the Riverine flooding region and each area, multipled by the area’s agricultural value density.”
[7] “RFLD_EXPT: An aggregation of population exposure, building exposure, and agricultural exposure to a hazard.”
[8] “RFLD_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Riverine Flooding event, or the average rate of loss associated with the occurrence of a Riverine Flooding Event.”
[9] “RFLD_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Riverine Flooding event, or the average rate of loss associated with the occurrence of a Riverine Flooding Event.”
[10] “RFLD_HLRA: The Historic Loss Ratio is the representative percentage of a location’s agricultural exposure area that experiences loss due to a Riverine Flooding event, or the average rate of loss associated with the occurrence of a Riverine Flooding Event.”
Tract hazards: Strong Wind
vars <- nri %>% select(contains("SWND"), -contains("HLRR")) %>% names()
nri %>% select(all_of(vars), TRACTFIPS) %>%
pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
geom_histogram() +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
facet_wrap(~measure, scales = "free")
meta %>%
filter(varname %in% vars, !(str_detect(about, "REMOVE"))) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()$label [1] “SWND_EVNTS: Strong Wind consists of damaging winds, often originating from thunderstorms, that are classified as exceeding 58 mph. The number of Strong Wind event-days are the number of days from 1986-2017 in which an area experienced strong winds, as compiled by the National Weather Service’s Severe Weather Database Files (https://www.spc.noaa.gov/wcm/).”
[2] “SWND_AFREQ: Strong Wind consists of damaging winds, often originating from thunderstorms, that are classified as exceeding 58 mph. The annualized frequency of Strong Wind event-days are the average number of days per year (based on historical events) from 1986-2017) in which an area experienced strong winds, as compiled by the National Weather Service’s Severe Weather Database Files (https://www.spc.noaa.gov/wcm/).” [3] “SWND_EXPB: Because Strong Wind can occur anywhere, the entire building value of an area is considered exposed to Strong Wind.”
[4] “SWND_EXPP: Because Strong Wind can occur anywhere, the entire population value of an area is considered exposed to Strong Wind.”
[5] “SWND_EXPPE: Population loss is expressed in dollars using the value of statistical life approach in which each fatality or ten injuries is treated as $7.4 million of economic loss, an inflation-adjusted Value of Statistical Life (VSL) used by FEMA”
[6] “SWND_EXPA: Because Strong Wind can occur anywhere, the entire agricultural value of an area is considered exposed to Strong Wind.”
[7] “SWND_EXPT: An aggregation of population exposure, building exposure, and agricultural exposure to a hazard.”
[8] “SWND_HLRB: The Historic Loss Ratio is the representative percentage of a location’s building exposure area that experiences loss due to a Strong Wind event-day, or the average rate of loss associated with the occurrence of a Strong Wind event-day.”
[9] “SWND_HLRP: The Historic Loss Ratio is the representative percentage of a location’s population exposure area that experiences loss due to a Strong Wind event-day, or the average rate of loss associated with the occurrence of a Strong Wind event-day.”
[10] “SWND_HLRA: The Historic Loss Ratio is the representative percentage of a location’s agricultural exposure area that experiences loss due to a Strong Wind event-day, or the average rate of loss associated with the occurrence of a Strong Wind event-day.”
Maps
Variation across tracts
Droughts
# DRGT
pal <- colorNumeric("plasma", reverse = TRUE, domain = cville_nri$DRGT_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cville_nri,
fillColor = ~pal(DRGT_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", cville_nri$NAME, "<br>",
"Ann. Freq.: ", round(cville_nri$DRGT_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = cville_nri$DRGT_AFREQ,
title = "Drought-#/year", opacity = 0.7)meta %>%
filter(varname == "DRGT_AFREQ") %>%
select(about) %>%
as.list()$about [1] “A Drought is a deficiency of precipitation over an extended period of time resulting in a water shortage. The annualized freqeuncy of Drought events are the average number of days per year (based on historical droughts from 2000-2017 ) in which an area experinced extreme drought or exceptional drought as identified by the National Drought Mitigation Center, U.S. Drought Monitor (https://droughtmonitor.unl.edu/).”
Heat Wave
# HWAV
pal <- colorNumeric("plasma", reverse = TRUE, domain = cville_nri$HWAV_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cville_nri,
fillColor = ~pal(HWAV_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", cville_nri$NAME, "<br>",
"Ann. Freq.: ", round(cville_nri$HWAV_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = cville_nri$HWAV_AFREQ,
title = "Heat Wave-#/year", opacity = 0.7)meta %>%
filter(varname == "HWAV_AFREQ") %>%
select(about) %>%
as.list()$about [1] “A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. The annualized frequency of Heat Wave events are the average number of days per year (based on historical events from 2005-2017) for which an area received an Excessive Heat or Heat alert by the National Weather Services, as compiled by the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/request/gis/watchwarn.phtml).”
Hurricane
# HRCN
pal <- colorNumeric("plasma", reverse = TRUE, domain = cville_nri$HRCN_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cville_nri,
fillColor = ~pal(HRCN_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", cville_nri$NAME, "<br>",
"Ann. Freq.: ", round(cville_nri$HRCN_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = cville_nri$HRCN_AFREQ,
title = "Hurricane-#/year", opacity = 0.7)meta %>%
filter(varname == "HRCN_AFREQ") %>%
select(about) %>%
as.list()$about [1] “A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front and maximum sustained winds of at least 74 miles per hour; also included are tropical storms for which wind speeds range from 39 to 74 mph. The annualized frequency of Hurricane events are the average number of events per year (based on historical events from 1851 and 2017) experienced by an area as complied by the National Hurrican Center’s HURDAT2 dataset (https://www.nhc.noaa.gov/data/).”
Riverine Flooding
# RFLD
pal <- colorNumeric("plasma", reverse = TRUE, domain = cville_nri$RFLD_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cville_nri,
fillColor = ~pal(RFLD_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", cville_nri$NAME, "<br>",
"Ann. Freq.: ", round(cville_nri$RFLD_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = cville_nri$RFLD_AFREQ,
title = "Riverine Flooding-#/year", opacity = 0.7)meta %>%
filter(varname == "RFLD_AFREQ") %>%
select(about) %>%
as.list()$about [1] “Riverine Flooding is when streams and rivers exceed the capacity of their natural or constructed channels to accommodate water flow and water overflows the banks, spilling into adjacent low-lying, dry land. The annualized freqeuncy of Riverine Flooding events are the average number of events per year (based on historical events from 1995 and 2016) experienced by an area as recorded in the National Weather Service Storm Events Database (https://www.ncdc.noaa.gov/stormevents/).”
Strong Wind
# SWND
pal <- colorNumeric("plasma", reverse = TRUE, domain = cville_nri$SWND_AFREQ) # viridis
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cville_nri,
fillColor = ~pal(SWND_AFREQ),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 2,
fillOpacity = 0.8,
bringToFront = T
),
popup = paste0("Tract Number: ", cville_nri$NAME, "<br>",
"Ann. Freq.: ", round(cville_nri$SWND_AFREQ, 2))
) %>%
addLegend("bottomright", pal = pal, values = cville_nri$SWND_AFREQ,
title = "Strong Wind-#/year", opacity = 0.7)meta %>%
filter(varname == "SWND_AFREQ") %>%
select(about) %>%
as.list()$about [1] “Strong Wind consists of damaging winds, often originating from thunderstorms, that are classified as exceeding 58 mph. The annualized frequency of Strong Wind event-days are the average number of days per year (based on historical events) from 1986-2017) in which an area experienced strong winds, as compiled by the National Weather Service’s Severe Weather Database Files (https://www.spc.noaa.gov/wcm/).”
Nota Bene
- Coastal flooding not relevant for this region (included for consistency with Eastern Shore work).
- Most hazard overall ratings are very low or no ratings, except for drought; relative to larger national variation, these do not represent high risks.
- Several hazard rates are dominated by regional effects, with little variation within the region.